V. Sankardoss

Bio: V. Sankardoss is an academic researcher from VIT University. The author has contributed to research in topics: Electronic speed control & Adaptive control. The author has an hindex of 3, co-authored 5 publications receiving 43 citations.

PMDC Motor Parameter Estimation Using Bio-Inspired Optimization Algorithms

Abstract: The precise estimation of the motor parameter is essential to design the appropriate controller. The main goal of this paper is to estimate the parameters of permanent magnet dc (PMDC) motor used in a wheelchair, applying standard as well as a dynamic particle swarm optimization (PSO), ant colony optimization (ACO), and artificial bee colony (ABC) along with experimental methods. The electromechanical, mechanical, and electrical parameters, such as torque constant, back-emf constant, moment of inertia, viscous friction coefficient, armature inductance, and resistance are estimated using both the experimental and optimization methods. The motor is modeled in Matlab/Simulink R2015a using the estimated motor parameters and studied the performance with different loading conditions starting from no-load to full-load. The simulated results of motor performance with estimated parameters are compared with the experimental load test results. The results showed that the PMDC motor parameters estimated from dynamic PSO with varying inertia weight as well as ABC algorithm have comparatively very less speed and current error than standard PSO, dynamic PSO with constant inertia weight, and ACO algorithms. Furthermore, parameters from dynamic PSO with varying inertia weight showed speed as well as current error less than 0.5%, and the ABC algorithm shown current error slightly more than 0.5%. However, the analysis of variance tests shown no significant difference in current and speed performance with parameter estimated from ABC and dynamic PSO with varying inertia weight. Furthermore, ABC algorithm convergence is faster than dynamic PSO with varying inertia weight. But parameters estimated from dynamic PSO with varying inertia weight are precise and may be appropriate for the design of the motor controllers.

Design and Low-Cost Implementation of an Electric Wheelchair Control

Abstract: This paper discusses the design and low-cost implementation of direction and speed controller for an electric wheelchair actuated using a permanent magnet direct current (PMDC) motor. Most ...

Parameter Estimation in High Performance Sensor Less Vector Controlled Drives

Abstract: An Artificial Intelligence (AI) based Estimator is robust to parameter variations and noise and it avoids the use of mathematical models. Such a system is not restricted by the many assumptions used in the conventional methods and is capable of mapping any degree of non linearity. It can also yield the results more quickly. By the application of minimum configuration it is possible to obtain cost effective simple solutions using FPGA. The conventional methods are direct synthesis from state equations, Model Reference Adaptive System (MRAS) and Flux Observers. All these techniques use complex mathematical model of the motor which includes many assumptions. The estimation is not robust to parameter variations. The time taken for computation is also long. In this paper to develop AI based Estimators to Estimate the Speed, Torque and Flux of an Induction Motor for DTC drives.

Analysis of Multichannel SSVEP for Different Stimulus Frequencies.

Abstract: Steady-state visual evoked potential (SSVEP)-based methods are gaining popularity in different fields like cognition and brain–computer interface (BCI) studies. The SSVEP is used in BCI due to less training time, more accuracy, and high information transfer rate. The properties of SSVEP depend on factors like shape, size, frequency, and color of the stimuli. In this work, the white/black square stimuli are studied from Oz, O1, O2, Pz, P1, and P2 channels to analyze the power and energy at four different frequencies. The Psychophysics Toolbox in MATLAB is used to design the stimuli. The average power and average energy from white/black square stimuli of different channels help in frequency selection of visual stimuli at different channels. Further, signals with high energy may be effective in BCI.

A Modified Artificial Bee Colony Algorithm

Abstract: To overcome the shortcoming of poor diversity of Artificial Bee Colony(ABC)algorithm,this paper presents a modified Artificial Bee Colony algorithm.Unlike ABC in which onlooker bees choose employed bees from the colony according to proportional fitness choosing strategy,in MABC,in order to decrease the selection pressure and further improve the diversity,it does not set onlooker bees,as a result of no selection pressure on employed bees(solutions).The simulation results show that the presented algorithm can ensure the colony diversity and improve the performances of ABC.

The Trace transform and its applications.

Abstract: The Trace transform proposed, a generalization of the Radon transform, consists of tracing an image with straight lines along which certain functionals of the image function are calculated. Different functionals that can be used may be invariant to different transformations of the image. The paper presents the properties the functionals must have in order to be useful in three different applications of the method: construction of invariant features to rotation, translation and scaling of the image, construction of sensitive features to the parameters of rotation, translation and scaling of the image, and construction of features that may correlate well with a certain phenomenon we wish to monitor.

Automatic Detection of Erythemato-Squamous Diseases Using k-Means Clustering

Abstract: A new approach based on the implementation of k-means clustering is presented for automated detection of erythemato-squamous diseases. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. The studied domain contained records of patients with known diagnosis. The k-means clustering algorithm's task was to classify the data points, in this case the patients with attribute data, to one of the five clusters. The algorithm was used to detect the five erythemato-squamous diseases when 33 features defining five disease indications were used. The purpose is to determine an optimum classification scheme for this problem. The present research demonstrated that the features well represent the erythemato-squamous diseases and the k-means clustering algorithm's task achieved high classification accuracies for only five erythemato-squamous diseases.

Parameter estimation of DC motor through whale optimization algorithm

Abstract: This article estimates the unknown dc motor parameters by adapting the adaptive model with the reference model created by experimental data onto armature current and speed response from separately excited dc motor .The field flux dynamics, which is usually ignored, is included to model the dynamics of the motor. The block diagram including the flux dynamics and model parameters is considered as the adaptive model. The integral time square error between the instant experimental data and the corresponding adaptive model data is taken as cost function. The Whale optimization algorithm is used to minimize the cost function. Additionally, to improve the performances of optimization algorithm and for accurate result, the experimental data is divided into three intervals which form the three inequality constraints. A fixed penalty value is added to the cost function for violating these constraints. The effectiveness of estimation with two different methods is validated by convergence curve.

Dynamic Analysis of a Permanent Magnet DC Motor Using a Buck Converter Controlled by ZAD-FPIC

Abstract: This paper presents the dynamic analysis of a permanent magnet DC motor using a buck converter controlled by zero average dynamics (ZADs) and fixed-point inducting control (FPIC). Initially, the steady-state behavior of the closed-loop system was observed and then transient behavior analyzed while maintaining a fixed ZAD control parameter and changing the FPIC parameter. Other behaviors were studied when the value of the ZAD control parameter changed and the FPIC parameter was maintained at the initial value. Besides, bifurcation diagrams were built with one and two delay periods by changing the control parameter of the FPIC and maintaining fixed ZAD parameters while some disturbances were carried out in the electric source. The results show that the ZAD-FPIC controller allowed good regulation of the speed for different reference values. The ZAD-FPIC control technique is effective for controlling the buck converter with the motor, even with two delay periods. The robustness of the system was checked by changing the voltage of the source. It was shown that the system used a fixed switching frequency because the duty cycle was not saturated for certain ranges of the control parameters shown in the research. This technique can be used for higher order systems with experimental phenomena such as quantization effects, time delays, and variations in the input signal.